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Virtual staining for pathology: Challenges, limitations and perspectives

Intelligent Oncology 2025年第2期1卷 105-119页

作者： Weiping Lin Yihuang Hu Runchen Zhu Baoshun Wang Liansheng Wang Department of Computer Science School of Informatics Xiamen University Xiamen Fujian 361005 China National Institute for Data Science in Health and Medicine Xiamen University Xiamen Fujian 361005 China

In pathological examinations, tissue must first be stained to meet specific diagnostic requirements, a meticulous process demanding significant time and expertise from specialists. With advancements in deep learning, this staining process can now be achieved through computational methods known as virtual staining. This technique replicates the visual effects of traditional histological staining in pathological imaging, enhancing efficiency and reducing costs. Extensive research in virtual staining for pathology has already demonstrated its effectiveness in generating clinically relevant stained images across a variety of diagnostic scenarios. Unlike previous reviews that broadly cover the clinical applications of virtual staining, this paper focuses on the technical methodologies, encompassing current models, datasets, and evaluation methods. It highlights the unique challenges of virtual staining compared to traditional image translation, discusses limitations in existing work, and explores future perspectives. Adopting a macro perspective, we avoid overly intricate technical details to make the content accessible to clinical experts. Additionally, we provide a brief introduction to the purpose of virtual staining from a medical standpoint, which may inspire algorithm-focused researchers. This paper aims to promote a deeper understanding of interdisciplinary knowledge between algorithm developers and clinicians, fostering the integration of technical solutions and medical expertise in the development of virtual staining models. This collaboration seeks to create more efficient, generalized, and versatile virtual staining models for a wide range of clinical applications.

关键词： Virtual staining Pathological image generation Models and datasets Model evaluation

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CiRLExplainer: Causality-Inspired Explainer for Graph Neural Networks via Reinforcement Learning

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IEEE Transactions on Neural Networks and Learning Systems 2025年第6期36卷 9970-9984页

作者： Wenya Hu Jia Wu Quan Qian School of Computer Engineering and Science Shanghai University Shanghai China School of Computing Macquarie University Sydney NSW Australia School of Computer Engineering and Science the Center of Materials Informatics and Data Science Materials Genome Institute the Key Laboratory of Silicate Cultural Relics Conservation Ministry of Education and Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai China

In this article, we propose a new graph neural network (GNN) explainability model, CiRLExplainer, which elucidates GNN predictions from a causal attribution perspective. Initially, a causal graph is constructed to analyze the causal relationships between the graph structure and GNN predicted values, identifying node attributes as confounding factors between the two. Subsequently, a backdoor adjustment strategy is employed to circumvent these confounders. Additionally, since the edges within the graph structure are not independent, reinforcement learning is incorporated. Through a sequential selection process, each step evaluates the combined effects of an edge and the previous structure to generate an explanatory subgraph. Specifically, a policy network predicts the probability of each candidate edge being selected and adds a new edge through sampling. The causal effect of this action is quantified as a reward, reflecting the interactivity among edges. By maximizing the policy gradient during training, the reward stream of the edge sequence is optimized. The CiRLExplainer is versatile and can be applied to any GNN model. A series of experiments was conducted, including accuracy (ACC) analysis of the explanation results, visualization of the explanatory subgraph, and ablation studies considering node attributes as confounding factors. The experimental results demonstrate that our model not only outperforms current state-of-the-art explanation techniques, but also provides precise semantic explanations from a causal perspective. Additionally, the experiments validate the rationale for considering node attributes as confounding factors, thereby enhancing the explanatory power and ACC of the model. Notably, across different datasets, our explainer achieved improvements over the best baseline models in the ACC-area under the curve (AUC) metrics by 5.89%, 5.69%, and 4.87%, respectively.

关键词： Predictive models Graph neural networks Reinforcement learning Accuracy Training Prediction algorithms Analytical models Sustainable development Focusing data science

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Mobility-aware Seamless Service Migration and Resource Allocation in Multi-edge IoV Systems

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IEEE Transactions on Mobile Computing 2025年第7期24卷 6315-6332页

作者： Chen, Zheyi Huang, Sijin Min, Geyong Ning, Zhaolong Li, Jie Zhang, Yan Fuzhou University College of Computer and Data Science Fuzhou350116 China Ministry of Education Engineering Research Center of Big Data Intelligence Fuzhou350002 China Fuzhou University Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350116 China University of Exeter Department of Computer Science Faculty of Environment Science and Economy ExeterEX4 4QF United Kingdom Chongqing University of Posts and Telecommunications School of Communications and Information Engineering Chongqing400065 China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China University of Oslo Department of Informatics Oslo0316 Norway

Mobile Edge Computing (MEC) offers low-latency and high-bandwidth support for Internet-of-Vehicles (IoV) applications. However, due to high vehicle mobility and finite communication coverage of base stations, it is hard to maintain uninterrupted and high-quality services without proper service migration among MEC servers. Existing solutions commonly rely on prior knowledge and rarely consider efficient resource allocation during the service migration process, making it hard to reach optimal performance in dynamic IoV environments. To address these important challenges, we propose SR-CL, a novel mobility-aware seamless Service migration and Resource allocation framework via Convex-optimization-enabled deep reinforcement Learning in multi-edge IoV systems. First, we decouple the Mixed Integer Nonlinear Programming (MINLP) problem of service migration and resource allocation into two sub-problems. Next, we design a new actor-critic-based asynchronous-update deep reinforcement learning method to handle service migration, where the delayed-update actor makes migration decisions and the one-step-update critic evaluates the decisions to guide the policy update. Notably, we theoretically derive the optimal resource allocation with convex optimization for each MEC server, thereby further improving system performance. Using the real-world datasets of vehicle trajectories and testbed, extensive experiments are conducted to verify the effectiveness of the proposed SR-CL. Compared to benchmark methods, the SR-CL achieves superior convergence and delay performance under various scenarios. © 2025 IEEE.

关键词： Convex optimization

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ChatGPT versus Google Gemini: a comparison to evaluate patient education guide created on common neurological disorders

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Discover Artificial Intelligence 2024年第1期4卷 79页

作者： Phillips, Vidith Kiryakoza, Fadi Arefin, Shamsul Choudhary, Nishtha Garifullin, Renat Division of Biomedical Informatics & amp Data Science Institute School of Medicine John Hopkins University BaltimoreMD United States Ibn Sena Teaching Hospital Mosul Iraq NHS Trust Nottingham Univesity Hospitals Nottingham United Kingdom Jhalawar Medical College Rajasthan Jhalawar India University of New Mexico AlbuquerqueNM United States

Introduction: This study assesses the effectiveness of AI tools, ChatGPT and Google Gemini, in educating the public about neurological conditions such as Bell’s palsy, Tetanus, and Headaches, and assess chatbot-generated patient guides based on readability and ease of understanding. Methodology: In March 2024, the authors conducted a cross-sectional study to develop patient education brochures for Bell’s palsy, Tetanus vaccination, and Persistent Headache, leveraging AI models ChatGPT and Google Gemini. The brochures’ quality was assessed through readability, similarity, and a modified DISCERN score for reliability. Statistical analysis, performed in R software, compared responses from both AI models using unpaired T-tests. The correlation between ease score and reliability was explored using Pearson’s Coefficient of Correlation. Results: The study revealed no significant variances in word count, sentence count, or average words per sentence between the two AI tools. However, materials generated by ChatGPT exhibited significantly higher ease scores, highlighting its proficiency in creating more understandable content (p © The Author(s) 2024.

关键词： Reliability analysis

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AGCL: Aspect Graph Construction and Learning for Aspect-level Sentiment Classification 31

AGCL: Aspect Graph Construction and Learning for Aspect-leve...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Jian, Zhongquan Wu, Daihang Wang, Shaopan Wang, Yancheng Yao, Junfeng Wang, Meihong Wu, Qingqiang Institute of Artificial Intelligence Xiamen University China School of Informatics Xiamen University China School of Film Xiamen University China College of Management Mahidol University Thailand School of Computing and Data Science Xiamen University Malaysia Malaysia Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism Xiamen University China Xiamen Key Laboratory of Intelligent Storage and Computing School of Informatics Xiamen University China

ISBN: (纸本)9798891761964

Prior studies on Aspect-level Sentiment Classification (ALSC) emphasize modeling interrelationships among aspects and contexts but overlook the crucial role of aspects themselves as essential domain knowledge. To this end, we propose AGCL, a novel Aspect Graph Construction and Learning method, aimed at furnishing the model with finely tuned aspect information to bolster its task-understanding ability. AGCL's pivotal innovations reside in Aspect Graph Construction (AGC) and Aspect Graph Learning (AGL), where AGC harnesses intrinsic aspect connections to construct the domain aspect graph, and then AGL iteratively updates the introduced aspect graph to enhance its domain expertise, making it more suitable for the ALSC task. Hence, this domain aspect graph can serve as a bridge connecting unseen aspects with seen aspects, thereby enhancing the model's generalization capability. Experiment results on three widely used datasets demonstrate the significance of aspect information for ALSC and highlight AGL's superiority in aspect learning, surpassing state-of-the-art baselines greatly. Code is available at https://***/jian-projects/agcl. © 2025 Association for Computational Linguistics.

关键词： Contrastive Learning

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A context-aware mobile augmented reality pet interaction model to enhance user experience

A context-aware mobile augmented reality pet interaction mod...

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作者： Luo, Yan Liu, Fang She, Yingying Yang, Baorong School of Informatics Xiamen University Xiamen China School of Film Xiamen University Xiamen China National Institute for Data Science in Health and Medicine Xiamen University Xiamen China College of Computer Engineering Jimei University Xiamen China

Virtual pet applications have been widely developed and applied in various fields. Mobile augmented reality (MAR) provides a new medium for virtual pets, allowing users to have a more immersive interactive experience through MAR pets. However, the issue of the user experience in MAR pets remains uninvestigated and relatively unexploited. Therefore, this article proposes a context-aware MAR pet interaction model (CAPet model) to enhance the user experience in MAR pet systems, which allows the MAR pet makes feedback adaptively corresponding to the dynamic context. In addition, this article presents a user experience pyramid to measure the user experience for MAR pet. According to the proposed CAPet model, a MAR pet-dog application is designed and implemented, based on which user test are conducted. The results of user test indicate the effectiveness of the proposed method on enhancing user experience, which provides a basis for the design of MAR pet applications in the future. © 2022 John Wiley & Sons Ltd.

关键词： Augmented reality

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Reversible data Hiding Using Convolutional Neural Network and Digital Signal Processing Techniques 12

Reversible Data Hiding Using Convolutional Neural Network an...

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12th International Conference on Information Technology in Medicine and Education, ITME 2022

作者： Qingmei, Peng Shaozi, Li Yaojin, Lin Xiehua, Yu University of Fujian Province Key Laboratory of Big Data and Artificial Intelligence MinNan Science and Technology University Quanzhou China Xiamen University School of Informatics Xiamen China Minnan Normal University School of Computer Science Lab of Data Science and Intelligence Application Zhangzhou China

ISBN: (纸本)9798350310153

Reversible data hiding is a data hiding process in which data is encrypted into an image as a means of authentication, and hidden data is used to hide the original image, and RDH is often used in areas where data authenticity and data preservation are important, such as legal/criminal investigations, medical images. In this study, this paper designed a novel reversible data hiding system that utilizes the Steghide algorithm to encrypt images and hide data, as well as an artificial neural network to recover embedded text and structure. Based on the analysis of the test results, it was concluded that the RDH scheme proposed in this article has the same or higher system accuracy and reliability as the existing RDH scheme. To further refine the system results in future studies, this paper proposes to increase the number of training iterations and the number of images in the training dataset to further improve the accuracy and reliability of the development. © 2022 IEEE.

关键词： Authentication

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Unite or divide? Biased search queries and Google Search results in polarized politics 25

Unite or divide? Biased search queries and Google Search res...

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Proceedings of the 17th ACM Web science Conference 2025

作者： Chau Tong School of Journalism University of Missouri Columbia USA and MU Institute for Data Science & Informatics University of Missouri Columbia USA

ISBN: (纸本)9798400714832

Research on search engine algorithmic curation has primarily focused on source diversity in response to general search terms, often overlooking the role of biased user input and its interaction with algorithmic filtering. Given that online users frequently engage in motivated information-seeking, it is crucial to examine how query variation influences search results and potentially reinforces information inequalities. This study employs a user-centered auditing approach through three real-world instances, where self-identified political partisans in the United States conducted Google searches using ideologically slanted queries related to the 2020 U.S. presidential election, abortion, and climate change. Analyzing search results across three topics and timepoints, the study finds that query slant—rather than user ideology—was the primary driver of search result differences. While personalization was observed, search results also exhibited a mainstreaming effect, often surfacing authoritative sources that may counteract polarization. However, slanted queries still led to distinct sets of information, raising concerns about algorithmic amplification of biased search behaviors. These findings contribute to the literature on algorithmic curation and information diversity, highlighting the need for ongoing audits of search engines to assess their role in shaping online political discourse and mitigating information inequalities in an era of heightened polarization.

关键词： Google Search

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Heterophilic Graph Representation Learning Based on Multi-Order Information Extraction and High and Low Pass Filters 10

Heterophilic Graph Representation Learning Based on Multi-Or...

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10th IEEE Smart World Congress, SWC 2024

作者： Wu, Ling Li, Xinyu Yang, Yingjie Guo, Kun Zhang, Qishan Fuzhou University College of Computer and Data Science Fuzhou China Fujian Key Laboratory of Network Computing and Intelligent Information Processing China De Montfort University School of Computer Science and Informatics Leicester United Kingdom Shanghai International Studies University College of Economics and Humanities Shanghai China

ISBN: (纸本)9798331520861

Real-world networks can be divided into homophilic and heterophilic graphs. Significantly, many nodes tend to be heterophilic in a homophilic graph. Graph representation learning in heterophilic graphs has attracted considerable significance in recent years as it breaks the homophily assumption and achieves higher-quality graph embeddings. However, existing heterophilic graph representation learning methods often mix attribute and topological information, neglecting the mutual exclusion between attribute information and topological information. Furthermore, algorithms in heterogeneous graphs usually rely on specific matrix structures to capture high-order information, which may restrict the applicability of these methods. To tackle these issues, this paper proposes a GNN-based strategy for Heterophilic graph Representation Learning based on Multi-order information extraction and High and Low pass filters (HRLMHL). First, we designed two relatively independent modules to learn attribute information and topological information, respectively, to address the issue of mismatch. Second, we built a multi-order information extraction module to extract multi-order similarity features, thereby achieving the learning of high-order graph information. Experiments on real datasets show that the node classification accuracy is superior to that of the state-of-the-art methods on most datasets. © 2024 IEEE.

关键词： Contrastive Learning

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Gastroenterology 2025年第5期168卷 1040-1041页

作者： Dennis L. Shung Section of Digestive Diseases Department of Medicine Yale School of Medicine New Haven Connecticut Department of Biomedical Informatics and Data Science Yale School of Medicine New Haven Connecticut

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